Taking advantage of class-specific feature selection

Authors:
Bárbara B. Pineda-Bautista;Jesús Ariel Carrasco-Ochoa;José Fco. Martínez-Trinidad
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, Mexico
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

In this work, a new method for class-specific feature selection, which selects a possible different feature subset for each class of a supervised classification problem, is proposed. Since conventional classifiers do not allow using a different feature subset for each class, the use of a classifier ensemble and a new decision rule for classifying new instances are also proposed. Experimental results over different databases show that, using the proposed method, better accuracies than using traditional feature selection methods, are achieved.